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Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis

The most accepted approach to sealing in high-level radioactive waste repositories (HLRWs) is to develop a low-pH grouting material with a pH of the pore solution of less than 11. Currently, the most widely used binary low-pH grouting material is MCSF64, which comprises 60% microfine cement (MC) and...

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Autores principales: Ren, Zengzeng, Zhao, Weiquan, Wang, Ju, Zhang, Jinjie, Chen, Liang, Li, Yonghui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220688/
https://www.ncbi.nlm.nih.gov/pubmed/37241518
http://dx.doi.org/10.3390/ma16103891
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author Ren, Zengzeng
Zhao, Weiquan
Wang, Ju
Zhang, Jinjie
Chen, Liang
Li, Yonghui
author_facet Ren, Zengzeng
Zhao, Weiquan
Wang, Ju
Zhang, Jinjie
Chen, Liang
Li, Yonghui
author_sort Ren, Zengzeng
collection PubMed
description The most accepted approach to sealing in high-level radioactive waste repositories (HLRWs) is to develop a low-pH grouting material with a pH of the pore solution of less than 11. Currently, the most widely used binary low-pH grouting material is MCSF64, which comprises 60% microfine cement (MC) and 40% silica fume (SF). In this study, a high-performance MCSF64-based grouting material was developed by incorporating naphthalene superplasticizer (NSP), aluminum sulfate (AS), and united expansion agent (UEA) to enhance the slurry’s shear strength, compressive strength, and hydration process. Orthogonal experiments were conducted to measure the flow time, yield stress, plastic viscosity, initial setting time, shear strength, and compressive strength of the MCSF64-based slurry, and the optimal mix proportion was determined using the Taguchi–Grey relational analysis method. The pH variation of the pore solution, shrinkage/expansion, and hydration products of the optimal hardened slurry were evaluated using simplified ex-situ leaching (S-ESL), a length comparometer, and scanning electron microscopy (SEM), respectively. The results demonstrate that the Bingham model effectively predicted the rheological properties of the MCSF64-based slurry. The optimum ratio for the MCSF64-based slurry was water/binder (W/B) ratio of 1.4, and the contents of NSP, AS and UEA by mass of binder were 1.9%, 3.6% and 4.8%, respectively. The optimal mix exhibited a pH value below 11 after curing for 120 days. The addition of AS and UEA facilitated hydration, shortened the initial setting time, improved early shear strength, and enhanced the expansion ability of the optimal mix under water curing conditions.
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spelling pubmed-102206882023-05-28 Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis Ren, Zengzeng Zhao, Weiquan Wang, Ju Zhang, Jinjie Chen, Liang Li, Yonghui Materials (Basel) Article The most accepted approach to sealing in high-level radioactive waste repositories (HLRWs) is to develop a low-pH grouting material with a pH of the pore solution of less than 11. Currently, the most widely used binary low-pH grouting material is MCSF64, which comprises 60% microfine cement (MC) and 40% silica fume (SF). In this study, a high-performance MCSF64-based grouting material was developed by incorporating naphthalene superplasticizer (NSP), aluminum sulfate (AS), and united expansion agent (UEA) to enhance the slurry’s shear strength, compressive strength, and hydration process. Orthogonal experiments were conducted to measure the flow time, yield stress, plastic viscosity, initial setting time, shear strength, and compressive strength of the MCSF64-based slurry, and the optimal mix proportion was determined using the Taguchi–Grey relational analysis method. The pH variation of the pore solution, shrinkage/expansion, and hydration products of the optimal hardened slurry were evaluated using simplified ex-situ leaching (S-ESL), a length comparometer, and scanning electron microscopy (SEM), respectively. The results demonstrate that the Bingham model effectively predicted the rheological properties of the MCSF64-based slurry. The optimum ratio for the MCSF64-based slurry was water/binder (W/B) ratio of 1.4, and the contents of NSP, AS and UEA by mass of binder were 1.9%, 3.6% and 4.8%, respectively. The optimal mix exhibited a pH value below 11 after curing for 120 days. The addition of AS and UEA facilitated hydration, shortened the initial setting time, improved early shear strength, and enhanced the expansion ability of the optimal mix under water curing conditions. MDPI 2023-05-22 /pmc/articles/PMC10220688/ /pubmed/37241518 http://dx.doi.org/10.3390/ma16103891 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ren, Zengzeng
Zhao, Weiquan
Wang, Ju
Zhang, Jinjie
Chen, Liang
Li, Yonghui
Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis
title Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis
title_full Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis
title_fullStr Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis
title_full_unstemmed Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis
title_short Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis
title_sort multi-response optimization of high-performance low-ph grouting materials by using taguchi-based grey relational analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220688/
https://www.ncbi.nlm.nih.gov/pubmed/37241518
http://dx.doi.org/10.3390/ma16103891
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